Systems and methods for actual gradient waveform estimation

ABSTRACT

The present disclosure provides a system for MRI. The system may obtain MRI scan data of a subject by directing an MRI scanner to perform an MRI scan on the subject according to a first gradient waveform. The system may also determine a second gradient waveform based on the first gradient waveform and a gradient waveform determination model. The gradient waveform determination model may have been trained according to a machine learning algorithm. The system may further generate a target reconstruction image of the subject based on the second gradient waveform and the MRI scan data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/243,587, filed on Apr. 29, 2021, the contents of each of which arehereby incorporated by reference.

TECHNICAL FIELD

The present disclosure generally relates to magnetic resonance imaging(MRI), and more particularly, systems and methods for actual gradientwaveform estimation in MRI.

BACKGROUND

MRI systems have been widely employed in disease diagnosis and/ortreatment. Normally, during an MRI scan (e.g., an ultra-short echo scan,a spiral MRI scan) of a subject, an actual gradient waveform applied tothe subject may be different from a preset gradient waveform planned tobe applied to the subject due to, for example, hardware limitations. Thedeviation of the actual gradient waveform with respect to the presetgradient waveform may affect the quality of a resulting image of the MRIscan. An approach to eliminate or reduce the effect of the deviation isto determine or estimate the actual gradient waveform, and perform imagereconstruction based on the actual gradient waveform. Therefore, it isdesirable to develop systems and methods for estimating an actualgradient waveform.

SUMMARY

According to an aspect of the present disclosure, a system for MRI isprovided. The system may include at least one storage device including aset of instructions for MRI and at least one processor configured tocommunicate with the at least one storage device. When executing the setof instructions, the at least one processor may be configured to directthe system to perform operations. The system may obtain MRI scan data ofa subject by directing an MRI scanner to perform an MRI scan on thesubject according to a first gradient waveform. The system may determinea second gradient waveform based on the first gradient waveform and agradient waveform determination model. The gradient waveformdetermination model may have been trained according to a machinelearning algorithm. The system may generate a target reconstructionimage of the subject based on the second gradient waveform and the MRIscan data.

In some embodiments, the determining a second gradient waveform based onthe first gradient waveform and a gradient waveform determination modelmay comprise determining a preliminary gradient waveform by processingthe first gradient waveform using at least one response function, anddetermining the second gradient waveform based on the preliminarygradient waveform and the gradient waveform determination model.

In some embodiments, the at least one response function may include anamplitude response function and a phase response function. Thedetermining a preliminary gradient waveform by processing the firstgradient waveform using at least one response function may comprisedetermining preliminary amplitude information of the preliminarygradient waveform by processing first amplitude information of the firstgradient waveform using the amplitude response function, and determiningpreliminary phase information of the preliminary gradient waveform byprocessing first phase information of the first gradient waveform usingthe phase response function.

In some embodiments, the gradient waveform determination model maycomprise an amplitude determination model and a phase determinationmodel. The determining the second gradient waveform MRI based on thepreliminary gradient waveform and the gradient waveform determinationmodel may comprise determining second amplitude information of thesecond gradient waveform by processing the preliminary amplitudeinformation using the amplitude determination model, and determiningsecond phase information of the second gradient waveform by processingthe preliminary amplitude information using the amplitude determinationmodel.

In some embodiments, the amplitude determination model may be trainedaccording to a first model training process. The first model trainingprocess may include obtaining a plurality of first training samples,obtaining a first preliminary model, and generating the amplitudedetermination model by training the first preliminary model using theplurality of first training samples. Each of the plurality of firsttraining samples may comprise sample first amplitude information of asample first gradient waveform planned to be applied to a sample subjectduring a sample MRI scan, and ground truth amplitude information of aground truth gradient waveform applied to the sample subject during thesample MRI scan.

In some embodiments, the generating the amplitude determination model bytraining the first preliminary model using the plurality of firsttraining samples may comprise: for each of the plurality of firsttraining samples, generating sample preliminary amplitude information ofthe sample first gradient waveform of the first training sample usingthe amplitude response function; and generating the amplitudedetermination model by training the first preliminary model using thesample preliminary amplitude information and the ground truth amplitudeinformation of each of the plurality of first training samples.

In some embodiments, the phase determination model may be trainedaccording to a second model training process. The second model trainingprocess may include obtaining a plurality of second training samples,obtaining a second preliminary model, and generating the phasedetermination model by training the second preliminary model using theplurality of second training samples. Each of the plurality of secondtraining samples may comprise sample first phase information of a samplefirst gradient waveform planned to be applied to a sample subject duringa sample MRI scan, and ground truth phase information of a ground truthgradient waveform applied to the sample subject during the sample MRIscan.

In some embodiments, the generating the phase determination model bytraining the second preliminary model using the plurality of secondtraining samples may comprise: for each of the plurality of secondtraining samples, generating sample preliminary phase information of thesample first gradient waveform of the second training sample using thephase response function; and generating the phase determination model bytraining the second preliminary model using the sample preliminary phaseinformation and the ground truth phase information of each of theplurality of second training samples.

In some embodiments, the amplitude determination model and the phasedetermination model are jointly trained according to a third modeltraining process. The third model training process may include obtaininga plurality of third training samples; generating a trained hybrid modelby training a third preliminary model that includes a first sub-modeland a second sub-model using the plurality of third training samples;and designating the trained first sub-model and the trained secondsub-model of the trained hybrid model as the amplitude determinationmodel and the phase determination model, respectively. Each of theplurality of third training samples may comprise sample first amplitudeinformation and sample first phase information of a sample firstgradient waveform planned to be applied to a sample subject during asample MRI scan, and ground truth amplitude information and ground truthphase information of a ground truth gradient waveform applied to thesample subject during the sample MRI scan.

In some embodiments, the amplitude determination model and the phasedetermination model may be convolutional neural network models.

In some embodiments, the MRI scan may be an ultrashort echo-time MRIscan or a spiral MRI scan.

According to another aspect of the present discourse, a system isprovided. The system may include at least one storage device storing aset of instructions for MRI and at least one processor configured tocommunicate with the at least one storage device. When executing the setof instructions, the at least one processor may be configured to directthe system to perform operations. The system may obtain a first gradientwaveform planned to be applied to a subject during an MRI scan. Thesystem may determine a second gradient waveform, the gradient waveformdetermination model having been trained according to a machine learningalgorithm based on the first gradient waveform and a gradient waveformdetermination model. The system may direct an MRI scanner to perform theMRI scan on the subject based on the second gradient waveform.

In some embodiments, the directing an MRI scanner to perform the MRIscan on the subject based on the second gradient waveform comprisesdetermining an adjusted gradient waveform by adjusting the firstgradient waveform according to the second gradient waveform, anddirecting the MRI scanner to perform the MRI scan on the subjectaccording to the adjusted gradient waveform.

In some embodiments, the amplitude determination model and the phasedetermination model may be trained separately or jointly.

In some embodiments, the amplitude determination model and the phasedetermination model may be convolutional neural network models.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities, andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. The drawings are not to scale. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary MRI systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a computing device according to some embodimentsof the present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a mobile device according to some embodiments ofthe present disclosure;

FIGS. 4A and 4B are block diagrams illustrating exemplary processingdevices according to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process for generating atarget reconstruction image of a subject according to some embodimentsof the present disclosure;

FIG. 6 is a flowchart illustrating an exemplary process for determininga second gradient waveform based on a first gradient waveform and agradient waveform determination model according to some embodiments ofthe present disclosure;

FIG. 7 is a schematic diagram illustrating an exemplary process fordetermining a second gradient waveform according to some embodiments ofthe present disclosure;

FIG. 8 is a flowchart illustrating an exemplary process for generatingan amplitude determination model according to some embodiments of thepresent disclosure;

FIG. 9 is a schematic diagram illustrating an exemplary process forgenerating an amplitude determination model according to someembodiments of the present disclosure;

FIG. 10 is a flowchart illustrating an exemplary process for generatinga phase determination model according to some embodiments of the presentdisclosure;

FIG. 11 is a flowchart illustrating an exemplary process for jointlygenerating an amplitude determination model and a phase determinationmodel according to some embodiments of the present disclosure; and

FIG. 12 is a flowchart illustrating an exemplary process for performingan MRI scan on a subject according to some embodiments of the presentdisclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well-known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, sections, or assembly ofdifferent levels in ascending order. However, the terms may be displacedby another expression if they achieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or another storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices (e.g., processor 210 as illustrated in FIG. 2) may beprovided on a computer-readable medium, such as a compact disc, adigital video disc, a flash drive, a magnetic disc, or any othertangible medium, or as a digital download (and can be originally storedin a compressed or installable format that needs installation,decompression, or decryption prior to execution). Such software code maybe stored, partially or fully, on a storage device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules/units/blocks may be includedin connected logic components, such as gates and flip-flops, and/or canbe included of programmable units, such as programmable gate arrays orprocessors. The modules/units/blocks or computing device functionalitydescribed herein may be implemented as software modules/units/blocks,but may be represented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description may beapplicable to a system, an engine, or a portion thereof.

It will be understood that when a unit, engine, module, or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

Provided herein are systems and components for non-invasive imagingand/or treatment, such as for disease diagnosis, treatment or researchpurposes. In some embodiments, the systems may include a radiotherapy(RT) system, a computed tomography (CT) system, an emission computedtomography (ECT) system, an X-ray photography system, a positronemission tomography (PET) system, or the like, or any combinationthereof. For illustration purposes, the disclosure describes systems andmethods for radiotherapy.

The term “image” in the present disclosure is used to collectively referto image data (e.g., scan data, projection data) and/or images ofvarious forms, including a two-dimensional (2D) image, athree-dimensional (3D) image, a four-dimensional (4D), etc. The term“pixel” and “voxel” in the present disclosure are used interchangeablyto refer to an element of an image. The term “anatomical structure” inthe present disclosure may refer to gas (e.g., air), liquid (e.g.,water), solid (e.g., stone), cell, tissue, an organ of a subject, or anycombination thereof, which may be displayed in an image (e.g., aplanning image, or a treatment image, etc.) and really exist in or onthe subject's body. The term “region,” “location,” and “area” in thepresent disclosure may refer to a location of an anatomical structureshown in the image or an actual location of the anatomical structureexisting in or on the subject's body, since the image may indicate theactual location of a certain anatomical structure existing in or on thesubject's body.

An MRI scan is often performed on a subject to collect MRI scan data ofthe subject for disease diagnosis and/or treatment. However, due tohardware limitations (e.g., an RF amplifier, a gradient delay, an eddy)of an MRI scanner, an actual gradient waveform applied to the subjectmay be different from a preset gradient waveform planned to be appliedto the subject. An MRI image may have a poor image quality (e.g.,include artifacts) if it is reconstructed based on the preset gradientwaveform. Conventionally, it is often assumed that the gradient systemof the MRI scanner fits a linear model, and the linear model is used topredict the actual gradient waveform. However, non-linear factors, suchas a non-linear effect of the power amplifier, a non-linear turbulence,or the like, or any combination thereof, may be omitted by theconventional approach. The actual gradient waveform prediction using thelinear model may have limited accuracy (e.g., result in imageartifacts), especially when a gradient field changes rapidly during theMRI scan.

An aspect of the present disclosure relates to systems and methods foractual gradient waveform estimation in MRI. The systems and methods mayobtain MRI scan data of a subject by directing an MRI scanner to performan MRI scan on the subject according to a first gradient waveform (orreferred to as a preset gradient waveform or an ideal gradientwaveform). The systems and methods may determine a second gradientwaveform based on the first gradient waveform and a gradient waveformdetermination model. The gradient waveform determination model may betrained according to a machine learning algorithm. The second gradientwaveform may be regarded as an estimation value of an actual gradientwaveform that is actually applied to the subject during the MRI scan.The systems and methods may further generate a target reconstructionimage of the subject based on the second gradient waveform and the MRIscan data.

In some embodiments, the systems and methods may perform actual gradientwaveform estimation before an MRI scan, and implement the MRI scan basedon the determination result. Specifically, the systems and methods mayobtain the first gradient waveform planned to be applied to the subject.The systems and methods may also determine the second gradient waveformbased on the first gradient waveform and the gradient waveformdetermination model. The systems and methods may further determine anadjusted gradient waveform by adjusting the first gradient waveformaccording to the second gradient waveform, and direct an MRI scanner toperform the MRI scan on the subject according to the adjusted gradientwaveform.

According to some embodiments of the present disclosure, the gradientwaveform determination model may be used to determine the secondgradient waveform so as to estimate the actual gradient waveform.Compared with a conventional liner model, the gradient waveformdetermination model, which learns an optimal mechanism for predictingthe second gradient waveform from training data, may have improvedaccuracy. The gradient waveform determination model may take linearfactors, non-linear factors, as well as complex factors (which areusually undetectable by human or traditional actual gradient waveformdetermination approaches) into consideration. The application of thegradient waveform determination model may improve the accuracy of thedetermined second gradient waveform, which in turn, the accuracy of theimage reconstruction performed based on the second gradient waveform orthe MRI scan performed based on the second gradient waveform.

FIG. 1 is a schematic diagram illustrating an exemplary MRI system 100according to some embodiments of the present disclosure. As shown inFIG. 1, the MRI system 100 may include an MRI scanner 110, a processingdevice 120, a storage device 130, one or more terminals 140, and anetwork 150. In some embodiments, the MRI scanner 110, the processingdevice 120, the storage device 130, and/or the terminal(s) 140 may beconnected to and/or communicate with each other via a wirelessconnection, a wired connection, or a combination thereof. Theconnections between the components in the MRI system 100 may bevariable. For example, the MRI scanner 110 may be connected to theprocessing device 120 through the network 150. As another example, theMRI scanner 110 may be connected to the processing device 120 directly.

The MRI scanner 110 may be configured to scan a subject (or a part ofthe subject) to acquire image data, such as echo signals (or MRIsignals) associated with the subject. In some embodiments, the MRIscanner 110 may include, for example, a main magnet, a gradient coil (oralso referred to as a spatial encoding coil), a radio frequency (RF)coil, etc.

In some embodiments, the MRI scanner 110 may include a gradient coilconfigured to apply a preset gradient waveform to the subject. However,due to equipment limitations, an actual gradient waveform applied to thesubject during the MRI scan may be different from the preset gradientwaveform. In some embodiments, the MRI scanner 110 may be a permanentmagnet MRI scanner, a superconducting electromagnet MRI scanner, or aresistive electromagnet MRI scanner, etc., according to the type of themain magnet. In some embodiments, the MRI scanner 110 may be ahigh-field MRI scanner, a mid-field MRI scanner, and a low-field MRIscanner, etc., according to the intensity of the magnetic field.

The subject scanned by the MRI scanner 110 may be biological ornon-biological. For example, the subject may include a patient, aman-made object, etc. As another example, the subject may include aspecific portion, organ, tissue, and/or a physical point of the patient.Merely by way of example, the subject may include head, brain, neck,body, shoulder, arm, thorax, cardiac, stomach, blood vessel, softtissue, knee, feet, or the like, or a combination thereof.

The processing device 120 may process data and/or information obtainedfrom the MRI scanner 110, the storage device 130, and/or the terminal(s)140. For example, the processing device 120 may determine a secondgradient waveform (i.e., an estimated actual gradient waveform) byapplying a gradient waveform determination model. As another example,the processing device 120 may generate the gradient waveformdetermination model by model training.

In some embodiments, a trained model (e.g., an amplitude determinationmodel and/or a phase determination model) may be generated by aprocessing device, while the application of the trained model may beperformed on a different processing device. In some embodiments, thetrained model may be generated by a processing device of a systemdifferent from the MRI system 100 or a server different from theprocessing device 120 on which the application of the trained model isperformed. For instance, the trained model may be generated by a firstsystem of a vendor who provides and/or maintains such a trained model,while actual gradient waveform estimation based on the provided trainedmodel may be performed on a second system of a client of the vendor. Insome embodiments, the application of the trained model may be performedonline in response to a request for determining a second gradientwaveform. In some embodiments, the trained model may be determined orgenerated offline.

In some embodiments, the trained model may be determined and/or updated(or maintained) by, e.g., the manufacturer of the MRI scanner 110 or avendor. For instance, the manufacturer or the vendor may load theamplitude determination model and/or the phase determination model intothe MRI system 100 or a portion thereof (e.g., the processing device120) before or during the installation of the MRI scanner 110 and/or theprocessing device 120, and maintain or update the amplitudedetermination model and/or the phase determination model from time totime (periodically or not). The maintenance or update may be achieved byinstalling a program stored on a storage device (e.g., a compact disc, aUSB drive, etc.) or retrieved from an external source (e.g., a servermaintained by the manufacturer or vendor) via the network 150. Theprogram may include a new model (e.g., a newly trained model) or aportion of a model that substitutes or supplements a correspondingportion of the model.

In some embodiments, the processing device 120 may be a single server ora server group. The server group may be centralized or distributed. Insome embodiments, the processing device 120 may be local or remote. Forexample, the processing device 120 may access information and/or datafrom the MRI scanner 110, the storage device 130, and/or the terminal(s)140 via the network 150. As another example, the processing device 120may be directly connected to the MRI scanner 110, the terminal(s) 140,and/or the storage device 130 to access information and/or data. In someembodiments, the processing device 120 may be implemented on a cloudplatform. For example, the cloud platform may include a private cloud, apublic cloud, a hybrid cloud, a community cloud, a distributed cloud, aninter-cloud, a multi-cloud, or the like, or a combination thereof. Insome embodiments, the processing device 120 may be implemented by acomputing device 200 having one or more components as described inconnection with FIG. 2.

The storage device 130 may store data, instructions, and/or any otherinformation. In some embodiments, the storage device 130 may store dataobtained from the MRI scanner 110, the processing device 120, and/or theterminal(s) 140. In some embodiments, the storage device 130 may storedata and/or instructions that the processing device 120 may execute oruse to perform exemplary methods described in the present disclosure. Insome embodiments, the storage device 130 may include a mass storagedevice, a removable storage device, a volatile read-and-write memory, aread-only memory (ROM), or the like, or a combination thereof. Exemplarymass storage devices may include a magnetic disk, an optical disk, asolid-state drive, etc. Exemplary removable storage devices may includea flash drive, a floppy disk, an optical disk, a memory card, a zipdisk, a magnetic tape, etc. Exemplary volatile read-and-write memory mayinclude a random access memory (RAM). Exemplary RAM may include adynamic RAM (DRAM), a double date rate synchronous dynamic RAM (DDRSDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), a zero-capacitorRAM (Z-RAM), etc. Exemplary ROM may include a mask ROM (MROM), aprogrammable ROM (PROM), an erasable programmable ROM (EPROM), anelectrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), a digital versatile disk ROM, etc. In some embodiments, thestorage device 130 may be implemented on a cloud platform as describedelsewhere in the disclosure.

In some embodiments, the storage device 130 may be connected to thenetwork 150 to communicate with one or more other components in the MRIsystem 100 (e.g., the MRI scanner 110, the processing device 120, and/orthe terminal(s) 140). One or more components of the MRI system 100 mayaccess the data or instructions stored in the storage device 130 via thenetwork 150. In some embodiments, the storage device 130 may be part ofthe processing device 120 or the terminal(s) 140.

The terminal(s) 140 may be configured to enable a user interactionbetween a user and the MRI system 100. For example, the terminal(s) 140may receive an instruction to cause the MRI scanner 110 to scan thesubject from the user. As another example, the terminal(s) 140 mayreceive a processing result (e.g., a slice image representative of aslice location of the subject) from the processing device 120 anddisplay the processing result to the user. In some embodiments, theterminal(s) 140 may be connected to and/or communicate with the MRIscanner 110, the processing device 120, and/or the storage device 130.In some embodiments, the terminal(s) 140 may include a mobile device140-1, a tablet computer 140-2, a laptop computer 140-3, or the like, ora combination thereof. For example, the mobile device 140-1 may includea mobile phone, a personal digital assistant (PDA), a gaming device, anavigation device, a point of sale (POS) device, a laptop, a tabletcomputer, a desktop, or the like, or a combination thereof. In someembodiments, the terminal(s) 140 may include an input device, an outputdevice, etc. The input device may include alphanumeric and other keysthat may be input via a keyboard, a touch screen (for example, withhaptics or tactile feedback), a speech input, an eye tracking input, abrain monitoring system, or any other comparable input mechanism. Theinput information received through the input device may be transmittedto the processing device 120 via, for example, a bus, for furtherprocessing. Other types of the input device may include a cursor controldevice, such as a mouse, a trackball, or cursor direction keys, etc. Theoutput device may include a display, a speaker, a printer, or the like,or a combination thereof. In some embodiments, the terminal(s) 140 maybe part of the processing device 120 or the MRI scanner 110.

The network 150 may include any suitable network that can facilitate theexchange of information and/or data for the MRI system 100. In someembodiments, one or more components of the MRI system 100 (e.g., the MRIscanner 110, the processing device 120, the storage device 130, theterminal(s) 140, etc.) may communicate information and/or data with oneor more other components of the MRI system 100 via the network 150. Forexample, the processing device 120 may obtain image data (e.g., an echosignal) from the MRI scanner 110 via the network 150. As anotherexample, the processing device 120 may obtain user instructions from theterminal(s) 140 via the network 150. The network 150 may include apublic network (e.g., the Internet), a private network (e.g., a localarea network (LAN), a wide area network (WAN)), etc.), a wired network(e.g., an Ethernet network), a wireless network (e.g., an 802.11network, a Wi-Fi network, etc.), a cellular network (e.g., a Long TermEvolution (LTE) network), a frame relay network, a virtual privatenetwork (“VPN”), a satellite network, a telephone network, routers,hubs, switches, server computers, or the like, or a combination thereof.For example, the network 150 may include a cable network, a wirelinenetwork, a fiber-optic network, a telecommunications network, anintranet, a wireless local area network (WLAN), a metropolitan areanetwork (MAN), a public telephone switched network (PSTN), a Bluetooth™network, a ZigBee™ network, a near field communication (NFC) network, orthe like, or a combination thereof. In some embodiments, the network 150may include one or more network access points. For example, the network150 may include wired and/or wireless network access points such as basestations and/or internet exchange points through which one or morecomponents of the MRI system 100 may be connected to the network 150 toexchange data and/or information.

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and characteristics of the exemplary embodimentsdescribed herein may be combined in various ways to obtain additionaland/or alternative exemplary embodiments. In some embodiments, the MRIsystem 100 may include one or more additional components and/or one ormore components described above may be omitted. Additionally oralternatively, two or more components of the MRI system 100 may beintegrated into a single component. For example, the processing device120 may be integrated into the MRI scanner 110. As another example, acomponent of the MRI system 100 may be replaced by another componentthat can implement the functions of the component. In some embodiments,the storage device 130 may be a data storage including cloud computingplatforms, such as a public cloud, a private cloud, a community andhybrid cloud, etc. However, those variations and modifications do notdepart the scope of the present disclosure.

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a computing device 200 according to someembodiments of the present disclosure. The computing device 200 may beused to implement any component of the MRI system 100 as describedherein. For example, the processing device 120 and/or the terminal(s)140 may be implemented on the computing device 200, respectively, viaits hardware, software program, firmware, or a combination thereof.Although only one such computing device is shown, for convenience, thecomputer functions relating to the MRI system 100 as described hereinmay be implemented in a distributed fashion on a number of similarplatforms, to distribute the processing load. As illustrated in FIG. 2,the computing device 200 may include a processor 210, a storage device220, an input/output (I/O) 230, and a communication port 240.

The processor 210 may execute computer instructions (e.g., program code)and perform functions of the processing device 120 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, data structures,procedures, modules, and functions, which perform particular functionsdescribed herein. For example, the processor 210 may process dataobtained from the MRI scanner 110, the terminal(s) 140, the storagedevice 150, and/or any other component of the MRI system 100. In someembodiments, the processor 210 may include one or more hardwareprocessors, such as a microcontroller, a microprocessor, a reducedinstruction set computer (RISC), an application specific integratedcircuits (ASICs), an application-specific instruction-set processor(ASIP), a central processing unit (CPU), a graphics processing unit(GPU), a physics processing unit (PPU), a microcontroller unit, adigital signal processor (DSP), a field programmable gate array (FPGA),an advanced RISC machine (ARM), a programmable logic device (PLD), anycircuit or processor capable of executing one or more functions, or thelike, or any combinations thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors, thus operations and/or method operations that are performedby one processor as described in the present disclosure may also bejointly or separately performed by the multiple processors. For example,if in the present disclosure the processor of the computing device 200executes both operation A and operation B, it should be understood thatoperation A and operation B may also be performed by two or moredifferent processors jointly or separately in the computing device 200(e.g., a first processor executes operation A and a second processorexecutes operation B, or the first and second processors jointly executeoperations A and B).

The storage device 220 may store data obtained from one or morecomponents of the MRI system 100. In some embodiments, the storagedevice 220 may include a mass storage device, a removable storagedevice, a volatile read-and-write memory, a read-only memory (ROM), orthe like, or any combination thereof. In some embodiments, the storagedevice 220 may store one or more programs and/or instructions to performexemplary methods described in the present disclosure. For example, thestorage device 220 may store a program for the processing device 120 toexecute for generating a gradient waveform determination model.

The I/O 230 may input and/or output signals, data, information, etc. Insome embodiments, the I/O 230 may enable a user interaction with theprocessing device 120. In some embodiments, the I/O 230 may include aninput device and an output device. The input device may includealphanumeric and other keys that may be input via a keyboard, a touchscreen (for example, with haptics or tactile feedback), a speech input,an eye tracking input, a brain monitoring system, or any othercomparable input mechanism. The input information received through theinput device may be transmitted to another component (e.g., theprocessing device 120) via, for example, a bus, for further processing.Other types of the input device may include a cursor control device,such as a mouse, a trackball, or cursor direction keys, etc. The outputdevice may include a display (e.g., a liquid crystal display (LCD), alight-emitting diode (LED)-based display, a flat panel display, a curvedscreen, a television device, a cathode ray tube (CRT), a touch screen),a speaker, a printer, or the like, or a combination thereof.

The communication port 240 may be connected to a network (e.g., thenetwork 150) to facilitate data communications. The communication port240 may establish connections between the processing device 120 and theMRI scanner 110, the terminal(s) 140, and/or the storage device 150. Theconnection may be a wired connection, a wireless connection, any othercommunication connection that can enable data transmission and/orreception, and/or any combination of these connections. The wiredconnection may include, for example, an electrical cable, an opticalcable, a telephone wire, or the like, or any combination thereof. Thewireless connection may include, for example, a Bluetooth™ link, aWi-Fi™ link, a WiMax™ link, a WLAN link, a ZigBee™ link, a mobilenetwork link (e.g., 3G, 4G, 5G), or the like, or a combination thereof.In some embodiments, the communication port 240 may be and/or include astandardized communication port, such as RS232, RS485, etc. In someembodiments, the communication port 240 may be a specially designedcommunication port. For example, the communication port 240 may bedesigned in accordance with the digital imaging and communications inmedicine (DICOM) protocol.

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device 300 according to someembodiments of the present disclosure. In some embodiments, a terminal140 and/or a processing device 120 may be implemented on a mobile device300, respectively. As illustrated in FIG. 3, the mobile device 300 mayinclude a communication platform 310, a display 320, a graphicsprocessing unit (GPU) 330, a central processing unit (CPU) 340, an I/O350, a memory 360, and a storage 390. In some embodiments, any othersuitable component, including but not limited to a system bus or acontroller (not shown), may also be included in the mobile device 300.In some embodiments, a mobile operating system 370 (e.g., iOS™,Android™′ Windows Phone™) and one or more applications 380 may be loadedinto the memory 360 from the storage 390 in order to be executed by theCPU 340. The applications 380 may include a browser or any othersuitable mobile apps for receiving and rendering information relating tothe MRI system 100. User interactions with the information stream may beachieved via the I/O 350 and provided to the processing device 120and/or other components of the MRI system 100 via the network 150.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. A computer with user interface elements may be used to implementa personal computer (PC) or any other type of work station or terminaldevice. A computer may also act as a server if appropriately programmed.

FIGS. 4A and 4B are block diagrams illustrating exemplary processingdevices 120A and 120B according to some embodiments of the presentdisclosure. In some embodiments, the processing devices 120A and 120Bmay be embodiments of the processing device 120 as described inconnection with FIG. 1. The processing device 120A may be configured toperform actual gradient waveform prediction by applying a gradientwaveform determination model. The processing device 1206 may beconfigured to generate the gradient waveform determination model bymodel training.

In some embodiments, the processing devices 120A and 120B may berespectively implemented on a processing unit (e.g., the processor 210illustrated in FIG. 2 or the CPU 340 as illustrated in FIG. 3). Merelyby way of example, the processing devices 120A may be implemented on aCPU 340 of a terminal device, and the processing devices 1206 may beimplemented on a computing device 200. As another example, theprocessing device 120A may be implemented on a computing device of theMRI system 100, while the processing device 120B may be part of a deviceor a system of the manufacturer of the MRI system 100, or a portionthereof (e.g., the MRI scanner 110). Alternatively, the processingdevices 120A and 1206 may be implemented on a same computing device 200or a same CPU 340. For example, the processing devices 120A and 120B maybe implemented on a same computing device 200.

As shown in FIG. 4A, the processing device 120A may include anacquisition module 401, a determination module 402, a generation module403, and a control module 404.

The acquisition module 401 may be configured to obtain informationrelating to the MRI system 100. For example, the acquisition module 401may obtain MRI scan data of a subject by directing an MRI scanner toperform an MRI scan of the subject according to a first gradientwaveform. The first gradient waveform refers to a gradient waveformplanned to be applied to the subject during the MRI scan. As anotherexample, the acquisition module 401 may be configured to obtain thefirst gradient waveform planned to be applied to the subject before theMRI scan is performed.

The determination module 402 may be configured to determine a secondgradient waveform based on the first gradient waveform and a gradientwaveform determination model. The second gradient waveform may be anestimated value of an actual gradient waveform applied to the subjectduring the MRI scan. In some embodiments, the determination module 402may be configured to determine a preliminary gradient waveform byprocessing the first gradient waveform using at least one responsefunction. The determination module 402 may be configured to determinethe second gradient waveform based on the preliminary gradient waveformand the gradient waveform determination model. More descriptionsregarding the determination of the second gradient waveform may be foundelsewhere in the present disclosure. See, e.g., operation 520 in FIG. 5and relevant descriptions thereof. In some embodiments, thedetermination module 402 may be configured to determine an adjustedgradient waveform by adjusting the first gradient waveform according tothe second gradient waveform. More descriptions regarding thedetermination of the adjusted gradient waveform may be found elsewherein the present disclosure. See, e.g., operation 1230 in FIG. 12 andrelevant descriptions thereof.

The generation module 403 may be configured to generate a targetreconstruction image of the subject based on the second gradientwaveform and the MRI scan data. More descriptions regarding thegeneration of the target reconstruction image may be found elsewhere inthe present disclosure. See, e.g., operation 530 in FIG. 5 and relevantdescriptions thereof.

The control module 404 may be configured to control one or morecomponents of the MRI system 100. For example, the control module 404may direct an MRI scanner to perform the MRI scan on the subjectaccording to the adjusted gradient waveform.

As shown in FIG. 4B, the processing device 120B may include anacquisition module 405 and a training module 406.

The acquisition module 405 may be configured to obtain training dataused in generating one or more trained models as disclosed herein, suchas a gradient waveform determination model, an amplitude determinationmodel, a phase determination model. For example, the acquisition module405 may be configured to obtain a plurality of first training samplesand a first preliminary model, which may be used to generate theamplitude determination model. As another example, the acquisitionmodule 405 may be configured to obtain a plurality of second trainingsamples and a second preliminary model, which may be used to generatethe phase determination model. As yet another example, the acquisitionmodule 405 may be configured to obtain a plurality of third trainingsamples and a third preliminary model comprising a first sub-model and asecond sub-model, which may be used to jointly generate the amplitudedetermination model and the phase determination model. More descriptionsregarding the training data may be found elsewhere in the presentdisclosure. See, e.g., FIGS. 8-11 and relevant descriptions thereof.

The training module 406 may be configured to generate one or moretrained models (e.g., machine learning models) by model training. Insome embodiments, the one or more trained models may be generatedaccording to a machine learning algorithm. The machine learningalgorithm may include but not be limited to an artificial neural networkalgorithm, a deep learning algorithm, a decision tree algorithm, anassociation rule algorithm, an inductive logic programming algorithm, asupport vector machine algorithm, a clustering algorithm, a Bayesiannetwork algorithm, a reinforcement learning algorithm, a representationlearning algorithm, a similarity and metric learning algorithm, a sparsedictionary learning algorithm, a genetic algorithm, a rule-based machinelearning algorithm, or the like, or any combination thereof. The machinelearning algorithm used to generate the one or more machine learningmodels may be a supervised learning algorithm, a semi-supervisedlearning algorithm, an unsupervised learning algorithm, or the like.More descriptions regarding the generation of the one or more trainedmodels may be found elsewhere in the present disclosure. See, e.g.,FIGS. 8-11 and relevant descriptions thereof.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. In someembodiments, the processing device 120A and the processing device 1206may share two or more of the modules, and any one of the modules may bedivided into two or more units. For instance, the processing devices120A and 1206 may share a same acquisition module, that is, theacquisition module 401 and the acquisition module 405 are a same module.In some embodiments, the processing device 120A and/or the processingdevice 1206 may include one or more additional modules, such as astorage module (not shown) for storing data. In some embodiments, theprocessing device 120A and the processing device 1206 may be integratedinto one processing device 120.

FIG. 5 is a flowchart illustrating an exemplary process for generating atarget reconstruction image of a subject according to some embodimentsof the present disclosure. In some embodiments, process 500 may beexecuted by the MRI system 100. For example, the process 500 may beimplemented as a set of instructions (e.g., an application) stored in astorage device (e.g., the storage device 150, the storage device 220,and/or the storage 390). In some embodiments, the processing device 120A(e.g., the processor 210 of the computing device 200, the CPU 340 of themobile device 300, and/or one or more modules illustrated in FIG. 4A)may execute the set of instructions, and when executing theinstructions, the processing device 120A may be configured to performthe process 500. The operations of the illustrated process presentedbelow are intended to be illustrative. In some embodiments, the process500 may be accomplished with one or more additional operations notdescribed and/or without one or more of the operations discussed.Additionally, the order of the operations of process 500 illustrated inFIG. 5 and described below is not intended to be limiting.

In 510, the processing device 120A (e.g., the acquisition module 401)may obtain MRI scan data of the subject by directing an MRI scanner toperform an MRI scan of the subject according to a first gradientwaveform.

The subject may be biological or non-biological. For example, thesubject may include a patient, a man-made object, etc. As anotherexample, the subject may include a specific portion, organ, tissue,and/or a physical point of the patient. Merely by way of example, thesubject may include head, brain, neck, body, shoulder, arm, thorax,cardiac, stomach, blood vessel, soft tissue, knee, feet, or the like, ora combination thereof.

In some embodiments, the processing device 120A may direct the MRIscanner (e.g., the MRI scanner 110) to perform the MRI scan on thesubject according to the first gradient waveform, and obtain MRI scandata from the MRI scanner. Alternatively, the MRI scan data may bepreviously collected and stored in a storage device (e.g., the storagedevice 130, the storage device 220, an external storage device). Theprocessing device 120A may retrieve the MRI scan data from the storagedevice. In some embodiments, the MRI scan may be an ultrashort echo-timeMRI or a spiral MRI scan.

The first gradient waveform refers to a gradient waveform planned to beapplied to the subject during the MRI scan. In some embodiments, thefirst gradient waveform may be defined by first amplitude informationand first phase information of the first gradient waveform. For example,the first amplitude information may include an amplitude of the firstgradient waveform at a plurality of time points. The first phaseinformation may include a phase of the first gradient waveform at theplurality of time points. In some embodiments, the first amplitudeinformation may be represented as a first vector, and the first phaseinformation may be represented as a second vector.

In some embodiments, the first gradient waveform may be determinedaccording to a default setting of the MRI system 100 or set manually bya user of the MRI system 100 via, e.g., a terminal (e.g., the terminal140). For example, the first gradient waveform may be selected by adoctor from a plurality of first gradient waveforms. Alternatively, thefirst gradient waveform may be determined by the processing device 120Abased on an actual condition. For example, the first gradient waveformmay be determined by the processing device 120A based on informationrelating to the subject, such as the scan region, the age, the bodyshape, or the like, or any combination thereof, of the subject. In someembodiments, the processing device 120A may obtain one or more scanningparameters (e.g., a field of view, a bandwidth) according to which theMRI scan is planned to be performed. The processing device 120A mayfurther determine the first gradient waveform based on the one or morescanning parameters.

In application, due to hardware limitations as described elsewhere inthe present application, an actual gradient waveform applied to thesubject may be different from the first gradient waveform. Imagereconstruction using the first gradient waveform may result in an MRIimage with low image quality (e.g., having artifacts). Therefore, asecond gradient waveform, which is an estimated value of the actualgradient waveform, may need to be determined and used to reconstruct atarget reconstruction image of the subject with improved image quality.

In 520, the processing device 120A (e.g., the determination module 402)may determine the second gradient waveform based on the first gradientwaveform and a gradient waveform determination model.

In some embodiments, the second gradient waveform may be defined bysecond amplitude information and second phase information of the secondgradient waveform. For example, the second amplitude information mayinclude an amplitude of the second gradient waveform at a plurality oftime points. The second phase information may include a phase of thesecond gradient waveform at the plurality of time points.

As used herein, a gradient waveform determination model refers to atrained model (e.g., a machine learning model) or an algorithmconfigured for determining information relating to a second gradientwaveform based on its input. For example, the processing device 120A mayinput the first amplitude information and the first phase information ofthe first gradient waveform into the gradient waveform determinationmodel, and the gradient waveform determination model may output thesecond amplitude information and the second phase information of thesecond gradient waveform. As another example, the processing device 120Amay determine a preliminary gradient waveform based on the firstgradient waveform and at least one response function. The processingdevice 120A may further input information of the preliminary gradientwaveform into the gradient waveform determination model, and thegradient waveform determination model may output the second amplitudeinformation and the second phase information of the second gradientwaveform. More descriptions regarding the preliminary gradient waveformmay be found elsewhere in the present disclosure. See, e.g., FIG. 6 andrelevant descriptions thereof.

In some embodiments, the gradient waveform determination model mayinclude an amplitude determination model and a phase determinationmodel. An amplitude determination model refers to a trained model (e.g.,a machine learning model) or an algorithm configured for determiningsecond amplitude information of a second gradient waveform based on itsinput. A phase determination model refers to a trained model (e.g., amachine learning model) or an algorithm configured for determiningsecond phase information of a second gradient waveform based on itsinput.

In some embodiments, the amplitude determination model and the phasedetermination model may be two independent models that are trainedseparately. In other words, the processing device 120A may obtain thetwo independent models, and determine the second gradient waveform basedon the two independent models. For example, the processing device 120Amay determine a first input of the amplitude determination model basedon the first gradient waveform, and determine the second amplitudeinformation of the second gradient waveform based on the first input andthe amplitude determination model. The processing device 120A may alsodetermine a second input of the phase determination model based on thefirst gradient waveform, and determine the second phase information ofthe second gradient waveform based on the second input and the phasedetermination model.

Alternatively, the amplitude determination model and the phasedetermination model may be two sub-models of a hybrid trained model,wherein the amplitude determination model and the phase determinationmodel may be jointly trained during the generation process of thetrained hybrid model. In other words, the processing device 120A mayobtain a single trained hybrid model including the amplitudedetermination model and the phase determination model, and determine thesecond gradient waveform based on the trained hybrid model. For example,the processing device 120A may determine a third input of the trainedhybrid model, and determine the second amplitude information and thesecond phase information of the second gradient waveform based on thetrained hybrid model and the third input. For the convenience ofdescriptions, the present disclosure uses the term “gradient waveformdetermination model” to collectively refer to the phase determinationmodel, the amplitude determination model, the trained hybrid model, orany combination of these models. More descriptions regarding thedetermination of the second gradient waveform based on the gradientwaveform determination model may be found elsewhere in the presentdisclosure. See, e.g., FIG. 7 and relevant descriptions thereof.

In some embodiments, the gradient waveform determination model may be amachine learning model according to a machine learning algorithm. Forexample, the gradient waveform determination model may include a neuralnetwork model, such as a convolutional neural network (CNN) model (e.g.,a full CNN model, V-net model, a U-net model, an AlexNet model, anOxford Visual Geometry Group (VGG) model, a ResNet model), a generativeadversarial network (GAN) model, or the like, or any combinationthereof. In some embodiments, the gradient waveform determination modelmay include one or more components for feature extraction and/or featurecombination, such as a fully convolutional block, a skip-connection, aresidual block, a dense block, or the like, or any combination thereof.

Exemplary machine learning algorithms may include an artificial neuralnetwork algorithm, a deep learning algorithm, a decision tree algorithm,an association rule algorithm, an inductive logic programming algorithm,a support vector machine algorithm, a clustering algorithm, a Bayesiannetwork algorithm, a reinforcement learning algorithm, a representationlearning algorithm, a similarity and metric learning algorithm, a sparsedictionary learning algorithm, a genetic algorithm, a rule-based machinelearning algorithm, or the like, or any combination thereof. The machinelearning algorithm used to generate the gradient waveform determinationmodel may be a supervised learning algorithm, a semi-supervised learningalgorithm, an unsupervised learning algorithm, or the like.

In some embodiments, the processing device 120A may obtain the gradientwaveform determination model from one or more components of the MRIsystem 100 (e.g., the storage device 130, the terminals(s) 140) or anexternal source via a network (e.g., the network 150). For example, thegradient waveform determination model may be previously trained by acomputing device (e.g., the processing device 120B), and stored in astorage device (e.g., the storage device 130, the storage device 220,and/or the storage 390) of the MRI system 100. The processing device120A may access the storage device and retrieve the gradient waveformdetermination model from the storage device. In some embodiments, theamplitude waveform determination model may be trained by a computingdevice (e.g., the processing device 120B) by performing process 800disclosed herein. The phase generation model may be trained by acomputing device (e.g., the processing device 120B) by performingprocess 1000 disclosed herein. The trained hybrid model may be trainedby a computing device (e.g., the processing device 120B) by performingprocess 1100 disclosed herein. Different models may be trained by a samecomputing device or different computing devices.

In 530, the processing device 120A (e.g., the generation module 403) maygenerate the target reconstruction image of the subject based on thesecond gradient waveform and the MRI scan data.

In some embodiments, the processing device 120A may generate k-spacedata by filling the MRI scan data into K-space according to the secondgradient waveform. The processing device 120A may further reconstructthe target reconstruction image of the subject based on the k-spacedata, for example, by performing an inverse Fourier transformation onthe k-space data. In some embodiments, the processing device 120A maygenerate the target reconstruction image using an MRI imagereconstruction algorithm.

It should be noted that the above description regarding the process 500is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, the process 500 may be accomplishedwith one or more additional operations not described and/or without oneor more of the operations discussed above. Merely by way of example, theprocess 500 may include an additional operation to transmit the targetreconstruction image to a terminal for display. As another example,operation 530 may be omitted.

FIG. 6 is a flowchart illustrating an exemplary process for determininga second gradient waveform based on a first gradient waveform and agradient waveform determination model according to some embodiments ofthe present disclosure. In some embodiments, the process 600 may beperformed to achieve at least part of operation 520 as described inconnection with FIG. 5.

In 610, the processing device 120A (e.g., the determination module 402)may determine a preliminary gradient waveform by processing the firstgradient waveform using at least one response function.

As used herein, a response function may include a linear function ormodel that can be used for actual gradient waveform prediction. The atleast one response function may take liner factors, such as a lineareffect of a power amplifier, a linear effect of turbulence, or the like,or any combination thereof, into consideration in the actual gradientwaveform prediction. However, non-linear factors, such as a non-lineareffect of the power amplifier, a non-linear turbulence, or the like, orany combination thereof, may be omitted by the at least one responsefunction. The actual gradient waveform prediction using the at least oneresponse function may have limited accuracy (e.g., result in imageartifact), especially when a gradient field changes rapidly during theMRI scan. Therefore, the present disclosure uses the at least oneresponse function to pre-process the first gradient waveform todetermine the preliminary gradient waveform, and further uses thegradient waveform determination model to determine the second gradientwaveform based on the preliminary gradient waveform. In this way, boththe linear factors and the non-linear factors may be taken intoconsideration, thereby improving the accuracy of the determined secondgradient waveform, and in turn, the accuracy of image reconstructionperformed based on the second gradient waveform. In addition, bypre-processing the first gradient waveform, the computation amount ofthe gradient waveform determination model may be reduced, which mayimprove the efficiency of the second gradient waveform determination.

The preliminary gradient waveform may be defined by preliminaryamplitude information and preliminary phase information of thepreliminary gradient waveform. For example, the preliminary amplitudeinformation may include an amplitude of the preliminary gradientwaveform a plurality of time points. The preliminary phase informationmay include a phase of the preliminary phase gradient waveform at theplurality of time points.

In some embodiments, the processing device 120A may input informationrelating to the first gradient waveform into the at least one responsefunction, and the at least one response function may output informationrelating to the preliminary gradient waveform. For example, the at leastone response function may include an amplitude response function and aphase response function. The processing device 120A may determine thepreliminary phase information of the preliminary gradient waveform byprocessing the first phase information of the first gradient waveformusing the phase response function. The processing device 120A maydetermine the preliminary amplitude information of the preliminarygradient waveform by processing the first amplitude information of thefirst gradient waveform using the amplitude response function.

In some embodiments, the at least one response function may bedetermined by the processing device 120A based on experimental data ofone or more experimental scans (e.g., actual scans or simulated scans)performed on one or more experimental subjects. Alternatively, the atleast one response function may be previously determined by theprocessing device 120A or another computing device, and stored in astorage device (e.g., the storage device 130, the storage device 220,and/or the storage 390). The processing device 120A may retrieve the atleast one response function from the storage device.

In 620, the processing device 120A (e.g., the determination module 402)may determine the second gradient waveform based on the preliminarygradient waveform and the gradient waveform determination model.

In some embodiments, as described in connection with FIG. 5, thegradient waveform determination model may include an amplitudedetermination model and a phase determination model. The processingdevice 120A may determine the second amplitude information of the secondgradient waveform by processing the preliminary amplitude informationusing the amplitude determination model. The processing device 120A maydetermine the second phase information of the second gradient waveformby processing the preliminary phase information using the phasedetermination model.

For illustration purposes, FIG. 7 illustrates a schematic diagram of anexemplary process for determining a second gradient waveform accordingto some embodiments of the present disclosure.

As shown in FIG. 7, the processing device 120A may process informationof the first gradient waveform using the amplitude response function andthe phase response function. For example, the first amplitudeinformation of the first gradient waveform may be processed by theamplitude response function to determine the preliminary amplitudeinformation of the preliminary gradient waveform. The first phaseinformation of the first gradient waveform may be processed by the phaseresponse function to determine the preliminary phase information of thepreliminary gradient waveform.

Then, the processing device 120A may process the preliminary amplitudeinformation using the amplitude determination model to determine thesecond amplitude information of the second gradient waveform. Theprocessing device 120A may process the preliminary phase informationusing the phase determination model to determine the second phaseinformation of the second gradient waveform.

It should be noted that the above descriptions regarding FIGS. 6 and 7are merely provided for the purposes of illustration, and not intendedto limit the scope of the present disclosure. For persons havingordinary skills in the art, multiple variations and modifications may bemade under the teachings of the present disclosure. However, thosevariations and modifications do not depart from the scope of the presentdisclosure. In some embodiments, the processing device 120A maydetermine the second gradient waveform based on the gradient waveformdetermination model without using the at least one response function.For example, the second amplitude information of the second gradientwaveform may be determined by processing the first amplitude informationof the first gradient waveform using the amplitude determination model;and the second phase information of the second gradient waveform may bedetermined by processing the first phase information of the firstgradient waveform using the phase determination model.

FIG. 8 is a flowchart illustrating an exemplary process for generatingan amplitude determination model according to some embodiments of thepresent disclosure. In some embodiments, process 800 may be executed bythe MRI system 100. For example, the process 800 may be implemented as aset of instructions (e.g., an application) stored in a storage device(e.g., the storage device 150, the storage device 220, and/or thestorage 390). In some embodiments, the processing device 120B (e.g., theprocessor 210 of the computing device 200, the CPU 340 of the mobiledevice 300, and/or one or more modules illustrated in FIG. 4B) mayexecute the set of instructions and accordingly be directed to performthe process 800.

In some embodiments, one or more operations of the process 800 may beperformed to achieve at least part of operation 520 as described inconnection with FIG. 5. In some embodiments, the process 800 may beperformed by another device or system other than the MRI system 100,e.g., a device or system of a vendor of a manufacturer. For illustrationpurposes, the implementation of the process 800 by the processing device120B is described as an example.

In 810, the processing device 120B (e.g., the acquisition module 405)may obtain a plurality of first training samples. Each of the pluralityof first training samples may include sample first amplitude informationof a sample first gradient waveform planned to be applied to a samplesubject during a sample MRI scan, and ground truth amplitude informationof a ground truth gradient waveform applied to the sample subject duringthe sample MRI scan.

In some embodiments, the sample subject of a first training sample maybe of the same type as or a different type from the subject as describedin connection with 510. For example, the subject may be the head of apatient, and the sample subject may be the head of another patient or aman-made object (e.g., a phantom). The sample MRI scan may be an actualMRI scan or a simulated MRI scan applied to the sample subject. Thesample first gradient waveform of the first training sample refers to afirst gradient waveform planned to be applied to the sample subject ofthe first training sample. For example, the sample first gradientwaveform may be defined by sample first amplitude information and samplefirst phase information. In some embodiments, a plurality of sample MRIscans with different scanning parameters (e.g., a FOV, a bandwidth, aresolution) may be performed on the sample subject, so that a pluralityof different sample first gradient waveforms with different shapes maybe applied to the sample subject in the sample MRI scans.

The ground truth gradient waveform of the first training sample refersto a measured value of an actual gradient waveform applied to the samplesubject in the sample MRI scan. For example, the ground truth gradientwaveform may be measured by magnetic field detecting device(s) duringthe sample MRI scan. As another example, the sample subject may be awater phantom, and the ground truth gradient waveform may be determinedbased on MRI signals collected during the sample MRI scan of the waterphantom. In some embodiments, the ground truth gradient waveform may bedefined by ground truth amplitude information and ground truth phaseinformation of the ground truth gradient waveform. The ground truthamplitude information of the ground truth gradient waveform may includean amplitude of the ground truth gradient waveform at each of aplurality of time points during the sample MRI scan. The ground truthphase information of the ground truth gradient waveform may include aphase of the ground truth gradient waveform at each of the plurality oftime points during the sample MRI scan.

In some embodiments, a first training sample (or a portion thereof) maybe previously generated by a computing device (e.g., the processingdevice 120B) and stored in a storage device (e.g., the storage device130, the storage device 220, the storage 390, or an external database).The processing device 120B may retrieve the first training sample (or aportion thereof) from the storage device. Alternatively, the firsttraining sample (or a portion thereof) may be generated by theprocessing device 120B.

In 820, the processing device 120B (e.g., the acquisition module 405)may obtain a first preliminary model.

In some embodiments, the first preliminary model may be of any type ofmodel (e.g., a machine learning model), for example, a neural networkmodel (e.g., a CNN model, a GAN model), or the like. The firstpreliminary model may include one or more model parameters. For example,the first preliminary model may be a CNN model and exemplary modelparameters of the preliminary model may include the number (or count) oflayers, the number (or count) of kernels, a kernel size, a stride, apadding of each convolutional layer, a loss function, or the like, orany combination thereof. Before training, the model parameter(s) of thefirst preliminary model may have their respective initial values. Forexample, the processing device 120B may initialize parameter value(s) ofthe model parameter(s) of the first preliminary model.

In 830, for each of the plurality of first training samples, theprocessing device 120B (e.g., the training module 406) may generatesample preliminary amplitude information of the sample first gradientwaveform of the first training sample using the amplitude responsefunction.

For example, the sample first amplitude information of the sample firstgradient waveform may be inputted into the amplitude response function,and the amplitude response function may output the sample preliminaryamplitude information. In some embodiments, the generation of the samplepreliminary amplitude information may be performed in a similar manneras that of the preliminary amplitude information of the preliminarygradient waveform as described in connection with operation 610, and thedescriptions thereof are not repeated here.

In 840, the processing device 120B (e.g., the training module 406) maygenerate the amplitude determination model by training the firstpreliminary model using the sample preliminary amplitude information andthe ground truth amplitude information of each of the plurality of firsttraining samples.

In some embodiments, the first preliminary model may be trainedaccording to a machine learning algorithm as described elsewhere in thisdisclosure (e.g., FIG. 5 and the relevant descriptions). For example,the processing device 1206 may generate the amplitude determinationmodel according to a supervised machine learning algorithm by performingone or more iterations to iteratively update the model parameter(s) ofthe first preliminary model. For illustration purposes, an exemplarycurrent iteration of the iteration(s) is shown in FIG. 9 and describedin the following description. The current iteration may be performedbased on at least a portion of the first training samples. In someembodiments, a same set or different sets of first training samples maybe used in different iterations in training the first preliminary model.For brevity, the first training samples used in the current iterationare referred to as target training samples.

As shown in FIG. 9, for each target training sample, sample preliminaryamplitude information may be determined by processing sample firstamplitude information of the sample first gradient waveform of thetarget training sample. In the current iteration, the updated firstpreliminary model generated in a previous iteration may be evaluated.For example, for each target training sample, the processing device 1206may determine predicted amplitude information of the ground truthgradient waveform of the target training sample by inputting the samplepreliminary amplitude information of the target training sample into theupdated first preliminary model. The processing device 1206 may thendetermine a value of a first loss function of the updated firstpreliminary model based on the predicted amplitude information and theground truth amplitude information of the ground truth gradient waveformof each target training sample.

The first loss function may be used to evaluate the accuracy andreliability of the updated first preliminary model, for example, thesmaller the first loss function is, the more reliable the updated firstpreliminary model is. Exemplary first loss functions may include an L1first loss function, a focal first loss function, a log first lossfunction, a cross-entropy first loss function, a Dice first lossfunction, etc. The processing device 1206 may further update thevalue(s) of the model parameter(s) of the updated first preliminarymodel to be used in a next iteration based on the value of the lossfunction according to, for example, a backpropagation algorithm.

In some embodiments, the one or more iterations may be terminated if atermination condition is satisfied in the current iteration. Anexemplary termination condition may be that the value of the lossfunction obtained in the current iteration is less than a predeterminedthreshold. Other exemplary termination conditions may include that acertain count of iterations is performed, that the loss functionconverges such that the differences of the values of the loss functionobtained in consecutive iterations are within a threshold, etc. If thetermination condition is satisfied in the current iteration, theprocessing device 1206 may designate the updated first preliminary modelas the amplitude determination model.

In some embodiments, operation 830 may be omitted, and the amplitudedetermination model may be generated by training the first preliminarymodel using the sample first amplitude information and the ground truthamplitude information of each first training sample. For example, duringthe iteration for generating the amplitude determination model, thepredicted amplitude information may be determined based on the samplefirst amplitude information and the updated first preliminary model.

FIG. 10 is a flowchart illustrating an exemplary process for generatinga phase determination model according to some embodiments of the presentdisclosure. In some embodiments, process 1000 may be executed by the MRIsystem 100. For example, the process 1000 may be implemented as a set ofinstructions (e.g., an application) stored in a storage device (e.g.,the storage device 150, the storage device 220, and/or the storage 390).In some embodiments, the processing device 120B (e.g., the processor 210of the computing device 200, the CPU 340 of the mobile device 300,and/or one or more modules illustrated in FIG. 4B) may execute the setof instructions and accordingly be directed to perform the process 1000.In some embodiments, one or more operations of the process 1000 may beperformed to achieve at least part of operation 520 as described inconnection with FIG. 5.

In 1010, the processing device 120B (e.g., the acquisition module 405)may obtain a plurality of second training samples.

Each of the plurality of second training samples may include samplefirst phase information of a sample first gradient waveform planned tobe applied to a sample subject during a sample MRI scan and ground truthphase information of a ground truth gradient waveform applied to thesample subject during the sample MRI scan. More descriptions regardingthe sample subject, the sample first gradient waveform, the sample firstphase information, the ground truth gradient waveform, and the groundtruth phase information may be found elsewhere in the presentdisclosure. See, e.g., operation 810 and relevant descriptions thereof.

In 1020, the processing device 120B (e.g., the acquisition module 405)may obtain a second preliminary model.

The second preliminary model may be similar to the first preliminarymodel as described in connection with operation 820. In someembodiments, the first and second preliminary models may be of the sametype or different types of models.

In 1030, for each of the plurality of second training samples, theprocessing device 120B (e.g., the training module 406) may generatesample preliminary phase information of the sample first gradientwaveform of the second training sample using the phase responsefunction.

For example, the sample first phase information of the sample firstgradient waveform may be inputted into the phase response function, andthe phase response function may output the sample preliminary phaseinformation. In some embodiments, the generation of the samplepreliminary phase information may be performed in a similar manner asthat of the preliminary phase information of the preliminary gradientwaveform as described in connection with operation 610, and thedescriptions thereof are not repeated here.

In 1040, the processing device 120B (e.g., the training module 406) maygenerate the phase determination model by training the secondpreliminary model using the sample preliminary phase information and theground truth phase information of each of the plurality of secondtraining samples.

The generation of the phase determination model may be performed in asimilar manner as that of the amplitude determination model as describedin connection with operation 840. For example, in an iteration forgenerating the phase determination model, an updated second preliminarymodel generated in a previous iteration may be evaluated. The updatedsecond preliminary model may be used to determine predicted phaseinformation of a second training sample. The predicted phase informationand the ground truth phase information of the second training sample maybe used to determine the value of a second loss function relating to thesecond preliminary model. The second loss function may be similar to thefirst loss function as described in connection with FIG. 8. The updatedsecond preliminary model may be updated based on the value of the secondloss function.

In some embodiments, operation 1030 may be omitted, and the phasedetermination model may be generated by training the second preliminarymodel using the sample first phase information and the ground truthphase information of each second training sample. For example, duringthe iteration for generating the phase determination model, thepredicted phase information may be determined based on the sample firstphase information and the updated second preliminary model.

FIG. 11 is a flowchart illustrating an exemplary process for jointlygenerating an amplitude determination model and a phase determinationmodel according to some embodiments of the present disclosure. In someembodiments, process 1100 may be executed by the MRI system 100. Forexample, the process 1100 may be implemented as a set of instructions(e.g., an application) stored in a storage device (e.g., the storagedevice 150, the storage device 220, and/or the storage 390). In someembodiments, the processing device 120B (e.g., the processor 210 of thecomputing device 200, the CPU 340 of the mobile device 300, and/or oneor more modules illustrated in FIG. 4B) may execute the set ofinstructions and accordingly be directed to perform the process 1100. Insome embodiments, one or more operations of the process 1100 may beperformed to achieve at least part of operation 520 as described inconnection with FIG. 5.

In 1110, the processing device 120B (e.g., the acquisition module 405)may obtain a plurality of third training samples.

Each of the plurality of third training samples may include sample firstamplitude information and sample first phase information of a samplefirst gradient waveform planned to be applied to a sample subject duringa sample MRI scan, and ground truth amplitude information and groundtruth phase information of a ground truth gradient waveform applied tothe sample subject during the sample MRI scan. More descriptionsregarding the sample subject, the sample first gradient waveform, thesample first amplitude information, the sample first phase information,the ground truth gradient waveform, the ground truth amplitudeinformation, and the ground truth phase information may be foundelsewhere in the present disclosure. See, e.g., operation 810 andrelevant descriptions thereof.

In 1120, the processing device 120B (e.g., the acquisition module 405)may obtain a third preliminary model comprising a first sub-model and asecond sub-model.

In some embodiments, the third preliminary model may be a preliminaryhybrid model including the first sub-model and the second sub-model tobe trained. The first sub-model may be trained as the amplitudedetermination model, and the second sub-model may be trained as thephase determination model. In some embodiments, the first and secondsub-models may be of the same type or different types of models. In someembodiments, the first sub-model may be similar to the first preliminarymodel as described in connection with operation 820, and the secondsub-model may be similar to the second preliminary model as described inconnection with operation 1020.

In 1130, the processing device 120B (e.g., the acquisition module 405)may generate a trained hybrid model by training the third preliminarymodel using the plurality of third training samples.

The generation of the trained hybrid model may be performed in a similarmanner as that of the amplitude determination model as described inconnection with operation 840. For example, in an iteration forgenerating the trained hybrid model, an updated third preliminary modelgenerated in a previous iteration may be evaluated. The updated thirdpreliminary model may be used to determine predicted phase informationand predicted amplitude information of a third training sample. Thepredicted phase information, the predicted amplitude information, theground truth phase information, and the ground truth phase informationof the third training sample may be used to determine the value of athird loss function relating to the third preliminary model. The thirdloss function may be similar to the first loss function as described inconnection with FIG. 8. The updated third preliminary model may beupdated based on the value of the third loss function. In someembodiments, the third loss function may include a first component formeasuring a difference between the ground truth amplitude informationand the predicted amplitude information, and a second component formeasuring a difference between the ground truth phase information andthe predicted phase information. The first component may be used toupdate the first sub-model of the updated third preliminary model, andthe second component may be used to update the second sub-model of theupdated third preliminary model.

In 1140, the processing device 120B (e.g., the training module 406) maydesignate the trained first sub-model of the trained hybrid model as theamplitude determination model.

In 1150, the processing device 120B (e.g., the training module 406) maydesignate the trained second sub-model of the trained hybrid model asthe phase determination model.

It should be noted that the above descriptions regarding FIGS. 8-11 aremerely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. In some embodiments, the process 800 may be accomplishedwith one or more additional operations not described and/or without oneor more of the operations discussed above.

For example, after a trained model (the amplitude determination model,the phase determination model, the trained hybrid model) is generated,the processing device 1206 may further test the trained model using aset of testing samples. As another example, the processing device 1206may update the trained model periodically or irregularly based on one ormore newly-generated training samples (e.g., new sample first gradientwaveform in MRI scan). As yet another example, a training sample (or aportion thereof) may be preprocessed before model training. Merely byway of example, one or more waveform signal processing operations (e.g.,linearization, denoising, filtering, sharpen, etc.) may be performed ona sample first gradient waveform. As still another example, beforeoperation 1130, the processing device 1206 may determine samplepreliminary amplitude information and sample preliminary phaseinformation of each third training sample, and train the thirdpreliminary model using the sample preliminary amplitude information,the sample preliminary phase information, the ground truth amplitudeinformation, and the ground truth phase information of each thirdtraining sample.

FIG. 12 is a flowchart illustrating an exemplary process for performingan MRI scan on a subject according to some embodiments of the presentdisclosure. In some embodiments, process 1200 may be executed by the MRIsystem 100. For example, the process 1200 may be implemented as a set ofinstructions (e.g., an application) stored in a storage device (e.g.,the storage device 150, the storage device 220, and/or the storage 390).In some embodiments, the processing device 120A (e.g., the processor 210of the computing device 200, the CPU 340 of the mobile device 300,and/or one or more modules illustrated in FIG. 4A) may execute the setof instructions and accordingly be directed to perform the process 1200.The operations of the illustrated process presented below are intendedto be illustrative. In some embodiments, the process 1200 may beaccomplished with one or more additional operations not described and/orwithout one or more of the operations discussed. Additionally, the orderof the operations of process 1200 illustrated in FIG. 12 and describedbelow is not intended to be limiting.

In 1210, the processing device 120A (e.g., the acquisition module 401)may obtain a first gradient waveform planned to be applied to a subject.For example, the processing device 120A may obtain first amplitudeinformation and first phase information of the first gradient waveform.More descriptions regarding the first gradient waveform may be foundelsewhere in the present disclosure. See, e.g., operation 510 andrelevant descriptions thereof.

In 1220, the processing device 120A (e.g., the determination module 402)may determine a second gradient waveform based on the first gradientwaveform and a gradient waveform determination model, wherein thegradient waveform determination model may have been trained according toa machine learning algorithm.

The second gradient waveform may be regarded as an estimated value of anactual gradient waveform that will be actually applied to the subjectduring the MRI scan when the MRI scan is performed according to thefirst gradient waveform. Operation 1220 may be performed in a similarmanner as operation 520 as described in connection with FIG. 5, and thedescriptions thereof are not repeated here.

In 1230, the processing device 120A (e.g., the control module 404) maydirect an MRI scanner to perform the MRI scan on the subject based onthe second gradient waveform.

In some embodiments, the processing device 120A may determine one ormore scanning parameters to achieve the second gradient waveform, anddirect the MRI scanner to perform the MRI scan on the subject based onthe one or more scanning parameters.

In some embodiments, the processing device 120A (e.g., the determinationmodule 404) may determine an adjusted gradient waveform by adjusting thefirst gradient waveform according to the second gradient waveform.Further, the processing device 120A (e.g., the control module 404) maydirect the MRI scanner to perform the MRI scan on the subject accordingto the adjusted gradient waveform.

As aforementioned, due to hardware limitations, an actual gradientwaveform applied to the subject during the MRI scan is usually differentfrom the ideal first gradient waveform. The processing device 120A maydetermine the adjusted gradient waveform according to the secondgradient waveform (i.e., an estimated actual gradient waveform when theMRI scan is performed according to the first gradient waveform), suchthat when the MRI scan is performed according to the adjusted gradientwaveform, an actual gradient waveform applied to the subject under theeffect of the hardware limitations may be as close to the first gradientwaveform as possible.

For example, by comparing the first gradient waveform and the secondgradient waveform, the processing device 120A may determine a rulereflecting how the hardware limitations will affect the application ofthe first gradient waveform, and adjust the first gradient waveformbased on the rule. Merely by way of example, if the rule indicates thatthe second gradient waveform drops rapidly in a period while the firstgradient waveform is in a stable state in the period, the processingdevice 120A may adjust the portion of the first gradient waveform in theperiod to increase. In such cases, when the MRI scan is performedaccording to the adjusted gradient waveform, an actual gradient waveformmay be close to the first gradient waveform even if the adjusted portiondrops due to hardware limitations in the period.

By determining the adjusted gradient waveform based on the secondgradient waveform and performing the MRI scan according to the adjustedgradient waveform, the actual gradient waveform applied to the subjectduring the MRI scan may be close to the ideal first gradient waveform,which may achieve a desired scan effect. In some embodiments, the MRIscan may be an ultrashort echo-time MRI or a spiral MRI scan. In someembodiments, the processing device 120A may further generate a targetreconstruction image of the subject based on scan data collected in theMRI scan and the first gradient waveform. As described in connectionwith operation 1230, an actual gradient waveform applied to the subjectduring the subject may be close to the first gradient waveform when theMRI scan is performed according to the first gradient waveform.Reconstructing the target reconstruction image based on the firstgradient waveform may improve the reconstruction accuracy.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer readable media having computer readableprogram code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2103, Perl, COBOL2102, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, for example, aninstallation on an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed object matter requires more features than areexpressly recited in each claim. Rather, inventive embodiments lie inless than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities or propertiesused to describe and claim certain embodiments of the application are tobe understood as being modified in some instances by the term “about,”“approximate,” or “substantially.” For example, “about,” “approximate,”or “substantially” may indicate ±1%, ±5%, ±10%, or ±20% variation of thevalue it describes, unless otherwise stated. Accordingly, in someembodiments, the numerical parameters set forth in the writtendescription and attached claims are approximations that may varydepending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the application are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspracticable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting effect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

1-20. (canceled)
 21. A system, comprising: at least one storage deviceincluding a set of instructions for generating a gradient waveformdetermination model; and at least one processor configured tocommunicate with the at least one storage device, wherein when executingthe set of instructions, the at least one processor is configured todirect the system to perform operations including: obtaining a pluralityof training samples; obtaining a preliminary model; and generating thegradient waveform determination model by training the preliminary modelusing the training samples, wherein each of the plurality of trainingsamples includes: sample information of a sample gradient waveformplanned to be applied to a sample subject during a sample MRI scan; andground truth information of a ground truth gradient waveform applied tothe sample subject during the sample MRI scan.
 22. The system of claim21, wherein the operations further include: obtaining MRI scan data of asubject by directing an MRI scanner to perform an MRI scan on thesubject according to a first gradient waveform; determining, based onthe first gradient waveform and the gradient waveform determinationmodel, a second gradient waveform; and generating, based on the secondgradient waveform and the MRI scan data, a target reconstruction imageof the subject.
 23. The system of claim 22, wherein the determining,based on the first gradient waveform and the gradient waveformdetermination model, a second gradient waveform comprises: determining apreliminary gradient waveform by processing the first gradient waveformusing at least one response function; and determining the secondgradient waveform based on the preliminary gradient waveform and thegradient waveform determination model.
 24. The system of claim 23,wherein the at least one response function includes an amplituderesponse function and a phase response function, and the determining apreliminary gradient waveform by processing the first gradient waveformusing at least one response function comprises: determining preliminaryamplitude information of the preliminary gradient waveform by processingfirst amplitude information of the first gradient waveform using theamplitude response function; and determining preliminary phaseinformation of the preliminary gradient waveform by processing firstphase information of the first gradient waveform using the phaseresponse function.
 25. The system of claim 24, wherein the gradientwaveform determination model comprises an amplitude determination modeland a phase determination model, and the determining the second gradientwaveform MRI based on the preliminary gradient waveform and the gradientwaveform determination model comprises: determining second amplitudeinformation of the second gradient waveform by processing thepreliminary amplitude information using the amplitude determinationmodel; and determining second phase information of the second gradientwaveform by processing the preliminary amplitude information using thephase determination model.
 26. The system of claim 21, wherein theoperations further include: obtaining a first gradient waveform plannedto be applied to a subject during an MRI scan; determining, based on thefirst gradient waveform and the gradient waveform determination model, asecond gradient waveform; and directing, based on the second gradientwaveform, an MRI scanner to perform the MRI scan on the subject.
 27. Thesystem of claim 26, wherein the directing, based on the second gradientwaveform, an MRI scanner to perform the MRI scan on the subjectcomprises: determining an adjusted gradient waveform by adjusting thefirst gradient waveform according to the second gradient waveform; anddirecting the MRI scanner to perform the MRI scan on the subjectaccording to the adjusted gradient waveform.
 28. The system of claim 26,wherein the determining, based on the first gradient waveform and thegradient waveform determination model, a second gradient waveformcomprises: determining a preliminary gradient waveform by processing thefirst gradient waveform using at least one response function; anddetermining the second gradient waveform based on the preliminarygradient waveform and the gradient waveform determination model.
 29. Thesystem of claim 28, wherein the at least one response function includesan amplitude response function and a phase response function, and thedetermining a preliminary gradient waveform by processing the firstgradient waveform using at least one response function comprises:determining preliminary amplitude information of the preliminarygradient waveform by processing first amplitude information of the firstgradient waveform using the amplitude response function; and determiningpreliminary phase information of the preliminary gradient waveform byprocessing first phase information of the first gradient waveform usingthe phase response function.
 30. The system of claim 29, wherein thegradient waveform determination model comprises an amplitudedetermination model and a phase determination model, and the determiningthe second gradient waveform MRI based on the preliminary gradientwaveform and the gradient waveform determination model comprises:determining second amplitude information of the second gradient waveformby processing the preliminary amplitude information using the amplitudedetermination model; and determining second phase information of thesecond gradient waveform by processing the preliminary amplitudeinformation using the phase determination model.
 31. The system of claim21, wherein: the gradient waveform determination model includes anamplitude determination model, the sample information of the samplegradient waveform includes sample amplitude information of the samplegradient waveform, and the ground truth information of the ground truthgradient waveform includes ground truth amplitude information of theground truth gradient waveform.
 32. The system of claim 31, wherein thegenerating the gradient waveform determination model by training thepreliminary model using the training samples comprises: for each of theplurality of training samples, generating sample preliminary amplitudeinformation of the sample gradient waveform of the training sample usingan amplitude response function; and generating the amplitudedetermination model by training the preliminary model using the samplepreliminary amplitude information and the ground truth amplitudeinformation of each of the plurality of training samples.
 33. The systemof claim 21, wherein: the gradient waveform determination model includesa phase determination model, the sample information of the samplegradient waveform includes sample phase information of the samplegradient waveform, and the ground truth information of the ground truthgradient waveform includes ground truth phase information of the groundtruth gradient waveform.
 34. The system of claim 33, wherein thegenerating the gradient waveform determination model by training thepreliminary model using the training samples comprises: for each of theplurality of training samples, generating sample preliminary phaseinformation of the sample gradient waveform of the training sample usinga phase response function; and generating the phase determination modelby training the preliminary model using the sample preliminary phaseinformation and the ground truth phase information of each of theplurality of training samples.
 35. The system of claim 21, wherein: thegradient waveform determination model includes an amplitudedetermination model and a phase determination model that are jointlytrained, the preliminary model is a hybrid model including a firstsub-model and a second sub-model, the sample information of the samplegradient waveform includes sample amplitude information and sample phaseinformation of the sample gradient waveform, the ground truthinformation of the ground truth gradient waveform includes ground truthamplitude information and ground truth phase information of the groundtruth gradient waveform, and the generating the gradient waveformdetermination model by training the preliminary model using the trainingsamples comprises: generating a trained hybrid model by training thepreliminary model using the training samples; and designating thetrained first sub-model and the trained second sub-model of the trainedhybrid model as the amplitude determination model and the phasedetermination model, respectively.
 36. The system of claim 35, whereinthe amplitude determination model and the phase determination model areconvolutional neural network models.
 37. The system of claim 21, whereinthe MRI scan is an ultrashort echo-time MRI scan or a spiral MRI scan.38. A method for generating a gradient waveform determination modelimplemented on a computing device having at least one processor and atleast one storage device, the method comprising: obtaining a pluralityof training samples; obtaining a preliminary model; and generating thegradient waveform determination model by training the preliminary modelusing the training samples, wherein each of the plurality of trainingsamples includes: sample information of a sample gradient waveformplanned to be applied to a sample subject during a sample MRI scan; andground truth information of a ground truth gradient waveform applied tothe sample subject during the sample MRI scan
 39. The method of claim38, wherein the method further includes: obtaining MRI scan data of asubject by directing an MRI scanner to perform an MRI scan on thesubject according to a first gradient waveform; determining, based onthe first gradient waveform and the gradient waveform determinationmodel, a second gradient waveform; and generating, based on the secondgradient waveform and the MRI scan data, a target reconstruction imageof the subject.
 40. A non-transitory computer readable medium,comprising a set of instructions for generating a gradient waveformdetermination model, wherein when executed by at least one processor,the set of instructions direct the at least one processor to effectuatea method, the method comprising: obtaining a plurality of trainingsamples; obtaining a preliminary model; and generating the gradientwaveform determination model by training the preliminary model using thetraining samples, wherein each of the plurality of training samplesincludes: sample information of a sample gradient waveform planned to beapplied to a sample subject during a sample MRI scan; and ground truthinformation of a ground truth gradient waveform applied to the samplesubject during the sample MRI scan.